# Import MINST data
import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

import tensorflow as tf
import matplotlib.pyplot as plt

# Parameters
learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1

# tf Graph Input
x = tf.placeholder("float", [None, 784]) # mnist data image of shape 28*28=784
y = tf.placeholder("float", [None, 10]) # 0-9 digits recognition => 10 classes

# Create model

# Set model weights
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

# Construct model
activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax

# Minimize error using cross entropy
cross_entropy = y*tf.log(activation)
cost = tf.reduce_mean\
       (-tf.reduce_sum\
        (cross_entropy,reduction_indices=1))

optimizer = tf.train.\
            GradientDescentOptimizer(learning_rate).minimize(cost) 

#Plot settings
avg_set = []
epoch_set=[]

# Initializing the variables
init = tf.initialize_all_variables()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)

    # Training cycle
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples/batch_size)
        # Loop over all batches
        for i in range(total_batch):
            batch_xs, batch_ys = \
                      mnist.train.next_batch(batch_size)
            # Fit training using batch data
            sess.run(optimizer, \
                     feed_dict={x: batch_xs, y: batch_ys})
            # Compute average loss
            avg_cost += sess.run(cost, \
                                 feed_dict={x: batch_xs, \
                                            y: batch_ys})/total_batch
        # Display logs per epoch step
        if epoch % display_step == 0:
            print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)
        avg_set.append(avg_cost)
        epoch_set.append(epoch+1)
    print "Training phase finished"

    plt.plot(epoch_set,avg_set, 'o', label='Logistic Regression Training phase')
    plt.ylabel('cost')
    plt.xlabel('epoch')
    plt.legend()
    plt.show()

    # Test model
    correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1))
    # Calculate accuracy
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    print "Model accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})
